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Machine-Learning-Based Predictor of Human–Bacteria Protein–Protein Interactions by Incorporating Comprehensive Host-Network Properties by Xianyi Lian; Shiping Yang; Hong Li; Chen Fu; Ziding Zhang is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Machine-Learning-Based Predictor of Human–Bacteria Protein–Protein Interactions by Incorporating Comprehensive Host-Network Properties about?

The large-scale identification of protein-protein interactions (PPIs) between humans and bacteria remains a crucial step in systematically understanding the underlying molecular mechanisms of bacterial infection. Computational prediction approaches are playing an increasingly important role in accelerating the identification of PPIs. Here, we developed a new machine-learning-based predictor of human- Yersinia pestis PPIs. First, three conventional sequence-based encoding schemes and two host network-property-related encoding schemes (i.e., NetTP and NetSS) were introduced. Motivated by previous human-pathogen PPI network analyses, we designed NetTP to systematically characterize the host proteins' network topology properties and designed NetSS to reflect the molecular mimicry strategy used by pathogen proteins. Subsequently, individual predictive models for each encoding scheme were inferred by Random Forest. Finally, through the noisy-OR algorithm, 5 individual models were integrated into a final powerful model with an AUC value of 0.922 in the 5-fold cross-validation. Stringent benchmark experiments further revealed that our model could achieve a better performance than two state

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Author

Xianyi Lian; Shiping Yang; Hong Li; Chen Fu; Ziding Zhang

Publisher

American Chemical Society (ACS)

Published

2019

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)